Obesity represents a major global health challenge, contributing substantially to cardiovascular disease and metabolic complications. Orforglipron, a novel oral non-peptide GLP-1 receptor agonist, has emerged as a promising therapeutic option for weight management and glycemic control. This meta-analysis evaluated the efficacy and safety of orforglipron in obese patients with or without type 2 diabetes mellitus (T2DM).

A comprehensive literature search was conducted across PubMed, Embase, Cochrane Library and ClinicalTrials.gov from inception to October 2025 to identify randomised controlled trials (RCTs) evaluating orforglipron in obese patients. Mean differences (MDs) and risk ratios (RRs) with 95% confidence intervals (CIs) were calculated using random-effects models.

Five RCTs comprising 4410 participants were included. Orforglipron demonstrated dose-dependent reductions in body weight from 2.48% at 3 mg to 9.8% at 45 mg, BMI from 0.89 to 3.62 kg/m², waist circumference from 1.57 to 6.9 cm, and HbA1c from 0.76% to 1.04% compared with placebo. Favourable lipid changes included reductions in total cholesterol (MD: -4.51% [95% CI: -6.91 to -2.11]), LDL-C (MD: -5.34% [95% CI: -7.10 to -3.58]), and triglycerides (MD: -10.07% [95% CI: -12.33 to -7.80]), with increased HDL-C (MD: 2.94% [95% CI: 1.38 to 4.49]). However, gastrointestinal adverse events were significantly more frequent at doses of 12 mg or higher and treatment discontinuation rates were highest at 24 mg (RR:4.61 [95% CI:1.6 to 13.33]) and 36 mg (RR:3.68 [95% CI:2.48 to 5.44]) doses. Serious adverse events and mortality rates were comparable to those with placebo.

Orforglipron significantly improved glycemic and lipid parameters in patients with obesity, demonstrating dose-dependent efficacy with maximal benefits at higher doses. While gastrointestinal tolerability remains a clinically important limitation requiring mitigation strategies, orforglipron represents a promising oral therapeutic option for comprehensive obesity and metabolic management.

Glucagon-like peptide-1 receptor agonist (GLP-1 RA) medications are efficacious in improving health outcomes in patients with type 2 diabetes mellitus (T2DM). As demand and costs have increased, questions have arisen around socioeconomic and geographic differences in adherence.

To understand these differences, pharmacy claims data were used to examine medication fills across urban and rural areas and Census tract-level income and racial make-up.

Data obtained from the Wisconsin Health Information Organization database included adults (aged ≥18 years) diagnosed with T2DM, with a prescription fill for a GLP-1 RA medication between January 1, 2020, and September 30, 2022. Adherence was defined as proportion of prescription days covered of 80% or greater for 1 year from first fill. Unadjusted and adjusted logistic regression was performed. Adherence was compared across urban and rural residence and Census tract-level median household income quintiles and race (majority Black residents vs nonmajority, majority Hispanic residents vs nonmajority, and majority White residents vs nonmajority). Adjusted models included age, sex, insurance provider, and number of comorbidities.

Patients (N = 7,265) were 56.3% female with a mean age of 55.3 (SD = 12.1) years. Place-based variables showed 38.2% lived in rural areas, 5.8% lived in areas with majority Black population, 2.0% with majority Hispanic population, and 87.9% with majority White population. Overall, 40.1% of patients were adherent 1 year following first prescription fill. Adjusted models showed that patients in tracts with the lowest incomes had lower odds of adherence compared with those living in the highest income tracts (odds ratio [OR] = 0.76; 95% CI = 0.59-0.98). Patients in areas with majority Black residents had lower odds of adherence (OR = 0.67; 95% CI = 0.52-0.85) than areas with less than a majority of Black residents, whereas patients living in areas with majority White residents had higher odds of adherence (OR = 1.31; 95% CI = 1.11-1.56) than areas with less than a majority of White residents.

This analysis highlights geographic differences in medication adherence in patients with T2DM. Patients living in high-income areas and areas with higher percentages of White residents had higher odds of adherence to GLP-1 RA medications. As demand for GLP-1 RA medications grow, future work that identifies reasons for disparities in adherence and potential strategies to improve adherence is critical.

To compare retinal thickness measurements obtained with the Optos Monaco and Heidelberg Spectralis optical coherence tomography (OCT) systems across 9 Early Treatment Diabetic Retinopathy Study (ETDRS) sectors in a cohort comprising normal eyes.

Paired OCT scans from 64 eyes of 32 participants with normal retinal findings were acquired on both devices. Thickness measurements were obtained for the central subfield and the inner and outer sectors of the superior, nasal, inferior, and temporal quadrants. Outcomes included mean thickness, mean interdevice difference (Heidelberg minus Monaco), Pearson correlation coefficients, and Bland-Altman analyses. Scatterplots and Bland-Altman plots were constructed to evaluate agreement and assess potential interchangeability.

The Heidelberg Spectralis yielded significantly greater retinal thickness values than the Optos Monaco in all ETDRS sectors (p < 0.001), with mean differences ranging from +16.9 µm (outer superior) to +26.8 µm (inner superior). Pearson correlation coefficients indicated strong positive agreement (r ≥ 0.8) for the central subfield and most inner sectors, and moderate to strong positive agreement (r ≥ 0.5) in a single outer sector. Bland-Altman analyses demonstrated a statistically significant systematic bias favoring greater measurements with Heidelberg in most quadrants, with limits of agreement indicating clinically relevant variability. Although the relative agreement was high, absolute differences limit direct interchangeability.

Optos Monaco and Heidelberg Spectralis exhibit strong linear correlation in retinal thickness measurements but show significant systematic differences. Interchangeable use requires the application of correction factors where segmentation variability may be greater.

Gestational diabetes mellitus (GDM) is a heterogeneous disorder that compromises maternal and offspring health. Conventional medical nutrition therapy focuses on nutrient composition and caloric targets but largely omits timing and individualized biology. This narrative review synthesizes mechanistic, epidemiologic and interventional evidence linking circadian biology and meal timing (chrononutrition) to maternal glycemic control. Observational cohorts associate late eating and breakfast skipping with worse glycemia, while pilot interventions and CGM-based studies indicate that front-loading carbohydrates, restricting evening carbohydrate, extending overnight fasting (≈10-12 h), and simple within-meal sequencing can reduce postprandial excursions and increase time-in-range. We propose a pragmatic, tiered clinical pathway in which routine second-trimester triage (50 g glucose challenge test and ultrasound abdominal subcutaneous fat thickness) identifies higher-risk women for short-term CGM phenotyping and prioritized chrononutrition counseling. Integrating phenotype-matched timing interventions with dietetic support and digital decision tools allows rapid, individualized adjustments informed by real-time glucose patterns and patient chronotype. In principle, this tiered strategy could improve daily glycemic profiles, reduce the need for pharmacotherapy, and translate into better neonatal outcomes if supported by larger randomized trials. Chrononutrition therefore offers a promising extension of standard care: simple, low-cost adjustments to "when" food is eaten, supported by digital tools, could allow nutrition therapy for GDM to become more precise, more responsive, and ultimately more effective for both mother and child. Key priorities include validating bedside and chrono-omic stratifiers, testing scalable delivery platforms, and ensuring equitable access to personalized chrononutrition in pregnancy.

Mucormycosis (MCM) is an opportunistic fungal infection in immunocompromised hosts, most commonly associated with poorly controlled diabetes mellitus (DM). We conducted a retrospective review of 45 MCM cases diagnosed between 2010 and 2023 at a referral center in Central California, a region with high DM prevalence and significant healthcare disparities. Clinical features, histopathology, microbiology, treatment, and outcomes were analyzed. Ninety-six percent of patients had DM, and 69% had no other predisposing condition. Glycemic control was markedly poor: 36% had HbA1c > 10%, and 61% had HbA1c > 8%. Diabetic ketoacidosis (DKA) was present in 19% of patients and associated with 100% mortality. Rhino-orbito-cerebral mucormycosis (ROCM) accounted for 60% of cases and carried a 70% mortality rate. Angioinvasion, confirmed in 62% of biopsied cases, significantly increased mortality (69% vs. 28%, p = 0.015). In-hospital mortality remained high at 58%, consistent with outcomes reported in other high-burden settings. Over 60% of patients identified as Hispanic. ZIP code-based analyses revealed that 75% of individuals lived in neighborhoods with Healthy Places Index (HPI) scores below the 25th percentile, and 64% resided in areas with a Social Deprivation Index (SDI) of 85 or higher, indicating entrenched structural disadvantage. Our findings highlight that MCM in Central California disproportionately affects individuals with uncontrolled DM living in socially deprived areas. These data underscore the need for early diagnosis, targeted antifungal therapy, and upstream public health interventions addressing diabetes management and healthcare access.

Red blood cell (RBC) deformability is a critical biophysical property that enables effective passage of RBCs through microvasculature and ensures proper oxygen delivery. Impairment of this property is associated with various pathological conditions, including type 2 diabetes mellitus (T2DM). In this study, we developed an automated microfluidic platform for high-throughput and real-time assessment of RBC deformability under controlled flow conditions. The device features a structured microchannel design and integrated imaging to quantify individual cell deformation responses. Comparative analyses of RBCs from healthy individuals and T2DM patients revealed significant reductions in deformability in the diabetic group. In vivo validation using a diabetic mouse model further confirmed the progressive decline in RBC deformability under chronic hyperglycemia. This microfluidic approach provides a robust and efficient tool for characterizing RBC mechanical properties, offering potential for disease monitoring and clinical diagnostic applications.

Integrating artificial intelligence (AI) with wearable sensor technologies can revolutionize the monitoring and management of various chronic diseases and acute conditions. AI-integrated wearables are categorized by their underlying sensing techniques, such as electrochemical, colorimetric, chemical, optical, and pressure/stain. AI algorithms enhance the efficacy of wearable sensors by offering personalized, continuous supervision and predictive analysis, assisting in time recognition, and optimizing therapeutic modalities. This manuscript explores the recent advances and developments in AI-powered wearable sensing technologies and their use in the management of chronic diseases, including COVID-19, Diabetes, and Cancer. AI-based wearables for heart rate and heart rate variability, oxygen saturation, respiratory rate, and temperature sensors are reviewed for their potential in managing COVID-19. For Diabetes management, AI-based wearables, including continuous glucose monitoring sensors, AI-driven insulin pumps, and closed-loop systems, are reviewed. The role of AI-based wearables in biomarker tracking and analysis, thermal imaging, and ultrasound device-based sensing for cancer management is reviewed. Ultimately, this report also highlights the current challenges and future directions for developing and deploying AI-integrated wearable sensors with accuracy, scalability, and integration into clinical practice for these critical health conditions.

Sjögren's Syndrome (SS) and Type 1 Diabetes (T1D) are autoimmune disorders that can co-occur in patients, leading to complex clinical presentations. Despite observational evidence of their co-occurrence, the underlying genetic mechanisms remain poorly understood. To investigate the shared genetic factors and pathways between SS and T1D, we conducted a comprehensive analysis using multiomic approaches. Conditional and conjunctional false discovery rate analyses were performed to identify genetic polygenicity and overlap between the two diseases. Functional annotation and pathway analysis identified SNPs with regulatory potential. Furthermore, Mendelian Randomization (MR) analyses were employed to investigate causal associations between gene expression and disease risk. Single-cell differential gene expression analysis was also employed to validate the associations of risk genes with T1D and SS. Our analysis identified 36 shared loci, revealing common genetic enrichment between SS and T1D. Functional annotation and pathway analysis revealed 52 credible genes involved in cysteine-related processes, apoptotic signalling and immune responses. MR analyses revealed that AC007283.5 was positively linked with both SS and T1D, while PLEKHM1 and CRHR1-T1 were negatively associated. Additionally, CERS2 was positively associated with SS, DEF6 was positively associated with T1D, and KANSL1-AS1 was negatively associated with T1D, indicating the presence of complex regulatory mechanisms. Moreover, Single-cell differential gene expression analysis confirmed the dysregulation of risk genes in SS and T1D. This study identified shared genetic factors and pathways underlying SS and T1D, highlighting cysteine-related processes and apoptotic signalling. The findings underscore the complex interplay of autoimmunity and the need for targeted treatments addressing their common mechanisms.

Real-world studies on insulin degludec/aspart (IDegAsp) have been conducted in some Southeast Asian populations; however, data specific to Indonesia remain limited. The aim of this study was to evaluate the efficacy, safety profiles, and real-world clinical experience of IDegAsp after five years of implementation in diabetes care in Indonesia.

This five-year, single-center, open-label, prospective, non-interventional study included adults with type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM) who had been on IDegAsp treatment for at least 12 months. Glycemic and metabolic outcomes-glycated hemoglobin (HbA1c), fasting plasma glucose (FPG), postprandial glucose (PPG), and body mass index (BMI)-were assessed at baseline, 3, 6, and 12 months. The safety was evaluated based on hypoglycemia incidence. Clinical rationale for IDegAsp initiation and regimen models were also documented.

A total of 550 individuals (T1DM: 48; T2DM: 502) were included. At 12 months, both groups had significant reductions in HbA1c (T1DM: -3.60%, T2DM: -3.32%), FPG (T1DM: -119.39 mg/dL, T2DM: -105.60 mg/dL), and PPG (T1DM: -190.87 mg/dL, T2DM: -180.10 mg/dL) (all p < 0.001 compared to baseline). Slight but statistically significant increases in BMI were observed in both groups (both p < 0.001). No episodes of hypoglycemia were reported among T1DM patients, whereas in the T2DM cohort, it occurred in 3.0% of cases comprising 1.4% with a single episode and 1.6% with two episodes with no severe hypoglycemia reported. The most frequent reasons for initiating IDegAsp included suboptimal HbA1c and PPG levels, with T2DM patients more often citing the need for flexible injection time or schedule.

IDegAsp demonstrated sustained glycemic improvement at 3-, 6-, and 12-months follow-ups with a favorable safety profile over one year, in both T1DM and T2DM populations in Indonesia. These findings support its utility in routine clinical practice, particularly among patients with unmet glycemic targets or complex treatment needs.

This study aimed to develop a diabetic retinopathy (DR) Prediction model using various machine learning algorithms incorporating the novel predictor Triglyceride-glucose index (TyG). Furthermore, the model was interpreted using the SHapley Additive exPlanations (SHAP) method.

Real-world data were collected from a general hospital in a major city and a county clinic, then divided into the DR Group (1392) and non-DR group (2358). Baseline data were collected, and variables were selected using Recursive Feature Elimination with Cross-Validation (RFECV). The performance of five machine learning algorithms, including Logistic Regression model (LR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and XGBoost (XGB), was assessed based on accuracy, sensitivity, specificity, and Area Under the Curve (AUC) of the Receiver Operating characteristic Curve (ROC). The optimal model was interpreted using SHAP.

LVM and LR demonstrated superior performance in both the test set and training set (ROC, 0.85 and 0.82, respectively). The top five predictors identified by SHAP analysis included TyG, Insulin therapy, HbA1c, Diabetes Course, HDL. HDL was identified as a protective factor, while the remaining factors were associated with retinopathy.

LR and SVM demonstrated the best performance. To our knowledge, this is the first machine learning-based DR prediction model integrating the triglyceride-glucose index (TyG) as a core predictor, overcoming limitations of insulin resistance (IR) assessment in resource-limited settings. TyG provides a cost-effective alternative to conventional IR biomarkers (e.g., HOMA-IR), enabling practical DR risk stratification in primary care.